1. Field of the Invention
The invention relates to the measurement of linear and angular motion with magnetic field in three dimensions. More specifically, the invention relates to the use of inertial/magnetic measurement devices in highly dynamic environments without the necessity of known location and local geomagnetic field for applications that only need angular position of one component relative to another (e.g., fire arm night scope relative to night vision goggles) while providing magnetically aided attitude (angular orientation) output.
2. Description of the Related Art
An inertial measurement unit (IMU) is a single device which is associated with a vehicle or other body. The device collects a combination of angular and linear acceleration data which is then sent to a processor. Two kinds of groups of sensors are typically included in the devices housing. The first group is an accelerometer triad. An accelerometer triad measures translational values using three sensors—each of which generates a separate analog signal representing a raw measured acceleration in one of the three dimensions. The second group of sensors includes three angular rate sensors (gyros) which each measure the angular velocity (rate) in one of the three dimensions.
A heading and attitude reference system (AHRS) is a single device which includes an IMU for inertial measurements and a microprocessor with embedded software to gather the IMU data and refine it to produce accurate measurements of heading, pitch, and roll angles with respect to a given local reference frame.
Some IMUs contain an accelerometer triad, a gyro triad and a magnetometer triad. In these devices the magnetic field measured by the magnetometers is blended with the data from the gyros and accelerometers to produce an AHRS.
Maintaining accuracy is a goal common to all modern-day IMU's and AHRS's. All IMU's experience noise-related errors from the accelerometers and gyros. The initial error caused by the drift and noise can be quite small. Other errors include turn-on residual bias, scale factor (SF), g-sensitive gyro bias, and angular random walk. When these errors are incorporated into integrated real time data over a period of time, however, accuracy can degrade significantly.
A first step in combating drift is using calibration and initialization techniques. Sensor bias, e.g., can be calibrated using factory settings for a strap-down IMU. For other IMUs, the earth's gravity field is used to calibrate and recalibrate sensor biases, scale factors, and other troubled parameters.
Onboard (or operational) calibration requires that known valid inputs be provided for the estimation of parameters. This is simple when angular rate and acceleration values are known, e.g., when the body is at rest. However, when new information is mixed with earlier information in a real time status, e.g., when the body is and has been in motion, Kalman filtering has been used to dynamically estimate (or calibrate) such parameters.
Kalman filter-aided systems use recursive data to estimate the real time state of the body using the noisy and biased data received from the inertial/magnetic measurement device sensors along with measurements of parameters that are related to the parameters (states) being estimated by the Kalman filter. In other words, previous data is used along with noisy new readings to formulate a more accurate estimation of the current states. Bias errors as well as the navigational states (e.g., position, velocity, and attitude) are initially defined. These initial states and their covariances are propagated out using covariance matrices to estimate the accuracy of the current readings.
In a prior art publication, U.S. Pat. No. 6,522,992 issued to McCall et al., magnetometer data is output to an attitude and heading module to be combined with gyro heading data. McCall does not disclose, however, the manner in which the magnetic data is used. Typically, this has been accomplished as follows: The magnetometer provides signal readings in all three dimensions proportional to the measured magnetic field vector. Pitch and roll data are then received from the attitude and heading module to form a transformation matrix from the body frame to a horizontal frame. Magnetic heading data is then calculated using the measurement vector expressed in the horizontal frame. This data is then output to the attitude and heading module so that it may be considered in the calculation of heading data along with the gyro-generated heading data. The heading estimations, however, depend upon roll and pitch values derived from sensed acceleration. The limitation of this approach to magnetic heading derivation is that roll and pitch estimation is highly dependent on “stillness” of the vehicle as well as the requirement of a gravity-only environment. In “non-still” environments, disturbances caused by non-gravitational forces on the vehicle, can significantly corrupt the roll and pitch estimation accuracy. In pure ballistic (free-fall) environments, gravitational acceleration is unobservable.
Thus, there is a need in the art for an IMU (or AHRS) which more effectively uses the earth's magnetic field in mitigating gyro drift and other inertial measurement errors.